The University of Southampton
University of Southampton Institutional Repository

Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery

Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery
Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery
Introduction: The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.

Methods: We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.

Results: A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.

Conclusions: Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards.
0885-6222
373-381
Breen, Michael
2a4241cd-4f16-4f7f-9165-1459ed2c8890
Stein, Dan J
81ae9dac-89c4-446a-bda0-73d12749be45
Baldwin, David
1beaa192-0ef1-4914-897a-3a49fc2ed15e
Breen, Michael
2a4241cd-4f16-4f7f-9165-1459ed2c8890
Stein, Dan J
81ae9dac-89c4-446a-bda0-73d12749be45
Baldwin, David
1beaa192-0ef1-4914-897a-3a49fc2ed15e

Breen, Michael, Stein, Dan J and Baldwin, David (2016) Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery. Human Psychopharmacology: Clinical and Experimental, 31 (5), 373-381. (doi:10.1002/hup.2546).

Record type: Article

Abstract

Introduction: The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.

Methods: We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.

Results: A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.

Conclusions: Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards.

Text
BreenMS_Manuscript.doc - Accepted Manuscript
Download (646kB)
Text
Breen_et_al-2016-Human_Psychopharmacology__Clinical_and_Experimental (002).pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 15 July 2016
e-pub ahead of print date: 20 September 2016
Published date: September 2016
Organisations: Clinical & Experimental Sciences

Identifiers

Local EPrints ID: 403980
URI: https://eprints.soton.ac.uk/id/eprint/403980
ISSN: 0885-6222
PURE UUID: f1b3b4b3-8b61-4663-9017-320e1e18c16d

Catalogue record

Date deposited: 19 Dec 2016 11:36
Last modified: 10 Jan 2018 05:06

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×